Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "34" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 28 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 28 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460011 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.748288 | 16.334222 | 7.170082 | 7.497576 | 13.344121 | 15.925464 | 1.662270 | 1.218655 | 0.0354 | 0.0468 | 0.0080 | nan | nan |
| 2460010 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.759578 | 17.818179 | 5.665560 | 6.296436 | 9.194798 | 10.461404 | 1.442091 | 1.105934 | 0.0368 | 0.0466 | 0.0076 | nan | nan |
| 2460009 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.783941 | 16.519871 | 6.564901 | 7.104938 | 7.276856 | 8.796774 | 1.034094 | 1.143545 | 0.0342 | 0.0468 | 0.0090 | nan | nan |
| 2460008 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 16.644499 | 20.231345 | 6.997018 | 7.663810 | 6.615199 | 7.748528 | 4.452129 | 5.199637 | 0.0374 | 0.0488 | 0.0078 | nan | nan |
| 2460007 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.380325 | 15.159709 | 5.487744 | 6.014276 | 5.901113 | 7.191883 | 1.745795 | 1.358184 | 0.0351 | 0.0471 | 0.0086 | nan | nan |
| 2459999 | not_connected | 0.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0322 | 0.0352 | 0.0024 | nan | nan |
| 2459998 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.523061 | 12.848175 | 4.607700 | 4.941319 | 7.931674 | 10.170391 | 1.339219 | 1.175437 | 0.0327 | 0.0435 | 0.0074 | nan | nan |
| 2459997 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.509463 | 13.993329 | 4.894661 | 5.398587 | 7.679167 | 9.578447 | 2.426820 | 1.687320 | 0.0348 | 0.0484 | 0.0092 | nan | nan |
| 2459996 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.765985 | 15.057330 | 6.509686 | 6.823987 | 7.256666 | 9.231118 | 0.568911 | 0.464860 | 0.0336 | 0.0467 | 0.0087 | nan | nan |
| 2459995 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.067512 | 15.304489 | 5.681861 | 6.106213 | 7.992638 | 9.445515 | 0.433655 | 0.251353 | 0.0372 | 0.0516 | 0.0096 | nan | nan |
| 2459994 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.531660 | 14.880365 | 4.787701 | 5.310088 | 7.753132 | 9.518731 | 0.342834 | 0.037532 | 0.0345 | 0.0451 | 0.0072 | nan | nan |
| 2459993 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.822717 | 14.062270 | 4.187264 | 4.635474 | 10.154252 | 10.899011 | 0.859447 | 1.650136 | 0.0315 | 0.0351 | 0.0025 | nan | nan |
| 2459991 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.808685 | 17.357968 | 4.579881 | 5.047853 | 9.165321 | 10.734323 | 0.860552 | 0.506575 | 0.0340 | 0.0433 | 0.0063 | nan | nan |
| 2459990 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.957501 | 14.286344 | 4.421689 | 4.792428 | 9.064703 | 11.013805 | 0.849882 | 0.324724 | 0.0362 | 0.0473 | 0.0075 | nan | nan |
| 2459989 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.745222 | 14.490022 | 3.915319 | 4.497004 | 8.000911 | 9.238790 | 0.638519 | 0.345898 | 0.0338 | 0.0437 | 0.0068 | nan | nan |
| 2459988 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.031151 | 16.951026 | 4.524590 | 4.867177 | 10.768279 | 13.181351 | 0.557236 | 0.233249 | 0.0327 | 0.0417 | 0.0066 | nan | nan |
| 2459987 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.675442 | 14.207623 | 4.515026 | 5.035365 | 6.402073 | 7.973636 | 2.882945 | 2.948024 | 0.0357 | 0.0456 | 0.0068 | nan | nan |
| 2459986 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.557605 | 17.396192 | 4.956300 | 5.356461 | 9.404877 | 11.264378 | 6.316895 | 9.912072 | 0.0342 | 0.0449 | 0.0074 | nan | nan |
| 2459985 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.400280 | 15.772232 | 4.632831 | 5.017271 | 7.213465 | 8.498678 | 1.628380 | 0.967079 | 0.0334 | 0.0721 | 0.0271 | nan | nan |
| 2459984 | not_connected | 100.00% | 100.00% | 98.05% | 0.00% | - | - | 12.680803 | 15.114718 | 4.919522 | 5.311053 | 9.632841 | 12.111157 | 5.124549 | 5.102676 | 0.0353 | 0.0845 | 0.0350 | nan | nan |
| 2459983 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.469096 | 14.824944 | 4.520629 | 4.814115 | 9.308541 | 11.163355 | 4.053816 | 6.681322 | 0.0352 | 0.0468 | 0.0080 | nan | nan |
| 2459982 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.530086 | 12.191280 | 3.970944 | 4.288343 | 4.518201 | 5.240514 | 2.397523 | 3.125597 | 0.0345 | 0.0453 | 0.0073 | nan | nan |
| 2459981 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.617858 | 13.688218 | 4.648739 | 4.933175 | 10.439400 | 12.329440 | 0.873933 | 0.424842 | 0.0359 | 0.0474 | 0.0077 | nan | nan |
| 2459980 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.429993 | 13.265664 | 4.191946 | 4.620279 | 9.041604 | 10.785900 | 5.275454 | 5.291159 | 0.0362 | 0.0479 | 0.0079 | nan | nan |
| 2459979 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.830318 | 13.776774 | 3.710456 | 4.191490 | 8.965606 | 10.112572 | 1.658678 | 1.394811 | 0.0359 | 0.0439 | 0.0068 | nan | nan |
| 2459978 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.917698 | 13.989825 | 4.076246 | 4.511077 | 9.418371 | 11.009921 | 3.753334 | 3.429514 | 0.0319 | 0.0413 | 0.0069 | nan | nan |
| 2459977 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.268648 | 14.736890 | 4.211614 | 4.680963 | 9.372412 | 11.379949 | 2.143820 | 1.892548 | 0.0361 | 0.0492 | 0.0091 | nan | nan |
| 2459976 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.171818 | 14.218774 | 4.312510 | 4.692568 | 9.407003 | 10.818864 | 1.033953 | 0.719734 | 0.0340 | 0.0443 | 0.0073 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 16.334222 | 13.748288 | 16.334222 | 7.170082 | 7.497576 | 13.344121 | 15.925464 | 1.662270 | 1.218655 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 17.818179 | 14.759578 | 17.818179 | 5.665560 | 6.296436 | 9.194798 | 10.461404 | 1.442091 | 1.105934 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 16.519871 | 13.783941 | 16.519871 | 6.564901 | 7.104938 | 7.276856 | 8.796774 | 1.034094 | 1.143545 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 20.231345 | 20.231345 | 16.644499 | 7.663810 | 6.997018 | 7.748528 | 6.615199 | 5.199637 | 4.452129 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 15.159709 | 12.380325 | 15.159709 | 5.487744 | 6.014276 | 5.901113 | 7.191883 | 1.745795 | 1.358184 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 12.848175 | 10.523061 | 12.848175 | 4.607700 | 4.941319 | 7.931674 | 10.170391 | 1.339219 | 1.175437 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 13.993329 | 11.509463 | 13.993329 | 4.894661 | 5.398587 | 7.679167 | 9.578447 | 2.426820 | 1.687320 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 15.057330 | 12.765985 | 15.057330 | 6.509686 | 6.823987 | 7.256666 | 9.231118 | 0.568911 | 0.464860 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 15.304489 | 13.067512 | 15.304489 | 5.681861 | 6.106213 | 7.992638 | 9.445515 | 0.433655 | 0.251353 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 14.880365 | 12.531660 | 14.880365 | 4.787701 | 5.310088 | 7.753132 | 9.518731 | 0.342834 | 0.037532 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 14.062270 | 13.822717 | 14.062270 | 4.187264 | 4.635474 | 10.154252 | 10.899011 | 0.859447 | 1.650136 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 17.357968 | 14.808685 | 17.357968 | 4.579881 | 5.047853 | 9.165321 | 10.734323 | 0.860552 | 0.506575 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 14.286344 | 14.286344 | 11.957501 | 4.792428 | 4.421689 | 11.013805 | 9.064703 | 0.324724 | 0.849882 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 14.490022 | 14.490022 | 11.745222 | 4.497004 | 3.915319 | 9.238790 | 8.000911 | 0.345898 | 0.638519 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 16.951026 | 16.951026 | 14.031151 | 4.867177 | 4.524590 | 13.181351 | 10.768279 | 0.233249 | 0.557236 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 14.207623 | 11.675442 | 14.207623 | 4.515026 | 5.035365 | 6.402073 | 7.973636 | 2.882945 | 2.948024 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 17.396192 | 17.396192 | 14.557605 | 5.356461 | 4.956300 | 11.264378 | 9.404877 | 9.912072 | 6.316895 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 15.772232 | 15.772232 | 13.400280 | 5.017271 | 4.632831 | 8.498678 | 7.213465 | 0.967079 | 1.628380 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 15.114718 | 12.680803 | 15.114718 | 4.919522 | 5.311053 | 9.632841 | 12.111157 | 5.124549 | 5.102676 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 14.824944 | 12.469096 | 14.824944 | 4.520629 | 4.814115 | 9.308541 | 11.163355 | 4.053816 | 6.681322 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 12.191280 | 10.530086 | 12.191280 | 3.970944 | 4.288343 | 4.518201 | 5.240514 | 2.397523 | 3.125597 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 13.688218 | 13.688218 | 11.617858 | 4.933175 | 4.648739 | 12.329440 | 10.439400 | 0.424842 | 0.873933 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 13.265664 | 13.265664 | 11.429993 | 4.620279 | 4.191946 | 10.785900 | 9.041604 | 5.291159 | 5.275454 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 13.776774 | 11.830318 | 13.776774 | 3.710456 | 4.191490 | 8.965606 | 10.112572 | 1.658678 | 1.394811 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 13.989825 | 13.989825 | 11.917698 | 4.511077 | 4.076246 | 11.009921 | 9.418371 | 3.429514 | 3.753334 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 14.736890 | 12.268648 | 14.736890 | 4.211614 | 4.680963 | 9.372412 | 11.379949 | 2.143820 | 1.892548 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Shape | 14.218774 | 14.218774 | 12.171818 | 4.692568 | 4.312510 | 10.818864 | 9.407003 | 0.719734 | 1.033953 |